A New Class of Reduced-Bias Generalized Hill Estimators

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Abstract
The estimation of the extreme value index (EVI) is a crucial task in the field of statistics of extremes, as it provides valuable insights into the tail behavior of a distribution. For models with a Pareto-type tail, the Hill estimator is a popular choice. However, this estimator is susceptible to bias, which can lead to inaccurate estimations of the EVI, impacting the reliability of risk assessments and decision-making processes. This paper introduces a novel reduced-bias generalized Hill estimator, which aims to enhance the accuracy of EVI estimation by mitigating the bias.
Funding Information
  • Fundação para a Ciência e a Tecnologia (UIDB/00297/2020, UIDP/00297/2020, UIDB/00006/2020)
  • project 10.54499/CEECINST/00054/2018/CP1522/CT0003 (UIDB/00297/2020, UIDP/00297/2020, UIDB/00006/2020)